Let $\mathcal{H}(k,n,p)$ be the distribution on $k$-uniform hypergraphs where every subset of $[n]$ of size $k$ is included as an hyperedge with probability $p$ independently. In this work, we design and analyze a simple spectral algorithm that certifies a bound on the size of the largest clique, $ω(H)$, in hypergraphs $H \sim \mathcal{H}(k,n,p)$. For example, for any constant $p$, with high probability over the choice of the hypergraph, our spectral algorithm certifies a bound of $\tilde{O}(\sqrt{n})$ on the clique number in polynomial time. This matches, up to $\textrm{polylog}(n)$ factors, the best known certificate for the clique number in random graphs, which is the special case of $k = 2$. Prior to our work, the best known refutation algorithms [CGL04, AOW15] rely on a reduction to the problem of refuting random $k$-XOR via Feige's XOR trick [Fei02], and yield a polynomially worse bound of $\tilde{O}(n^{3/4})$ on the clique number when $p = O(1)$. Our algorithm bypasses the XOR trick and relies instead on a natural generalization of the Lovasz theta semidefinite programming relaxation for cliques in hypergraphs.